Nesrin Dogan, Ph.D.
Professor of Radiation Oncology
Description of Research
Dr. Dogan’s research focuses on Image-Guided Adaptive Radiation Therapy (IGART) which has a potential to enhance radiotherapy soft tissue targeting accuracy and for increased normal tissue sparing. A differentiating feature of IGART is its systematic integration of four-dimensional (4D) representations of patient anatomy and generalized descriptions of anatomic uncertainty into all aspects of planning and delivery decision-making, including plan adaptations to mitigate the effects of inter- and intra-fraction soft-tissue deformation and setup error. IGART implementations can take many forms, including online and offline treatment interventions ranging from simple geometric targeting corrections to full adaptive replanning. Dr. Dogan was the Core leader of a NIH Program Project Grant (PPG) on IGART for prostate, lung and cervix cancer patients. IGART poses radiation oncology with new and challenging demands: a high volume of imaging data drawn from many different modalities; new clinical tools, such as deformable image registration, which must perform reliably in an automated mode; the need for Intensity Modulated Radiation Therapy planning software that rapidly create optimal, deliverable and dosimetrically accurate plans with little or no user intervention. Dr. Dogan and her group developed technologically advanced software engineering, intensity modulated radiation treatment (IMRT) planning and quality assurance (QA) systems — essentially, an infrastructure that addresses the unique and substantial data-management needs, error pathways, and logistical constraints posed by online IGART.
Her group also developed a variety of QA tests which included automated dosimetric review techniques and assessed the quality of nonrigid registrations and fusions of multiple image sets as well as the quality of daily IMRT plan reoptimization schemes for IGART. Additional areas of interest include the assessment of online serial kV imaging and deformable image registration techniques for Head and Neck (H&N) Cancer patients. A large literature on H&N patients demonstrates that geometric uncertainty caused by tissue-displacement and patient daily setup errors limits the ability to focus the dose on the tumor only in H&N patients. By using serial kV Fan Beam CT and Cone Beam CT imaging and deformable image registration to describe the location of the target anatomy, IGART permits significant reduction of Clinical Target Volume margins, in turn permitting additional dose-per-fraction escalation and normal-tissue avoidance and highest level of dose conformity achievable. This maximizes the potential of IMRT to escalate biologically effective doses to precisely defined soft-tissue targets, corresponding either with primary tumor or more extended regional lymph-node targets. As a part of this project, her group built a H&N patient database for the validation of IGART processes using deformable image registration and Virtual Clinical Trials (VCTs). The assessment of volumetric assessment of soft tissue changes (including tumor shrinkage, soft tissue volume change) and the inter- and intrafractional setup errors are being analyzed. Based on this preliminary work done by Dr. Dogan and her group, the Virtual Clinical Trials (VCTs) which will investigate the benefits of adaptive IGRT in H&N patients in terms of target coverage and normal tissue avoidance and the feasibility of mounting a phase I/II trial to assess the clinical benefits of IGART in terms of acute and late toxicities and tumor control are being developed.